covid and policy

app.py

@@ -1,9 +1,11 @@
 import streamlit as st
 import pandas as pd
 import plotly.express as px
-import sqlite3
+#import bar_chart_race as bcr
 from raceplotly.plots import barplot
 
+
+
 # Configuration and Constants
 COUNTRY_MAPPING = {
     "Italy, San Marino and the Holy See": "Italy",
@@ -17,27 +19,19 @@ COUNTRY_MAPPING = {
 
 DEFAULT_COUNTRIES = ["Italy", "France", "Germany"]
 YEAR_RANGE = (2000, 2020)
-SQLITE_DB_PATH = "data.db"  # Path to the SQLite database
 
 # Data Loading and Processing Functions
 @st.cache_data
-def load_data(table_name):
-    # Connect to the SQLite database
-    conn = sqlite3.connect(SQLITE_DB_PATH)
-    query = f"SELECT * FROM '{table_name}'"
-    df = pd.read_sql(query, conn)
-    conn.close()
-    
-    # Replace country names according to the mapping
+def load_data(sheet_name, year_range = (2000, 2020), sector=None):
+    df = pd.read_excel("dati/fossilco2emission.xlsx", sheet_name=sheet_name)
     df_mapped = df.copy()
     df_mapped['Country'] = df_mapped['Country'].replace(COUNTRY_MAPPING)
     
-    # Build the list of year columns based on YEAR_RANGE. 
-    # (Assumes the table columns for years are named as numbers)
-    #year_cols = list(range(YEAR_RANGE[0], YEAR_RANGE[1] + 1))
-    year_cols = [str(year) for year in range(YEAR_RANGE[0], YEAR_RANGE[1] + 1)]
-
-    selected_cols = ['Country'] + year_cols
+    year_cols = list(range(year_range[0], year_range[1] + 1))
+    if sector:
+        selected_cols = ['Country', 'Sector'] + year_cols
+    else:
+        selected_cols = ['Country'] + year_cols
     return df_mapped[selected_cols].copy()
 
 def process_data_for_line_plot(df, selected_countries, year_range):
@@ -46,7 +40,7 @@ def process_data_for_line_plot(df, selected_countries, year_range):
     
     df_melted = filtered_df.melt(
         id_vars=['Country'],
-        value_vars=[str(year) for year in range(year_range[0], year_range[1] + 1)],
+        value_vars=range(year_range[0], year_range[1] + 1),
         var_name='Year',
         value_name='Emissions'
     )
@@ -70,7 +64,7 @@ def create_line_plot(data):
 def create_animated_choropleth(data, start_year, end_year):
     df_map = data.melt(
         id_vars=['Country'],
-        value_vars=[str(year) for year in range(start_year, end_year + 1)],
+        value_vars=range(start_year, end_year + 1),
         var_name='Year',
         value_name='Emissions'
     )
@@ -83,7 +77,7 @@ def create_animated_choropleth(data, start_year, end_year):
         animation_frame='Year',
         title='CO2 Emissions per Capita Over Time',
         color_continuous_scale='Reds',
-        range_color=[0, 25],
+        range_color=[0, df_map['Emissions'].quantile(0.95)],
         labels={'Emissions': 'CO2 Emissions per Capita (Mton)'}
     )
     
@@ -97,27 +91,29 @@ def create_animated_choropleth(data, start_year, end_year):
                 dict(label='Play',
                      method='animate',
                      args=[None, {'frame': {'duration': 500, 'redraw': True},
-                                  'fromcurrent': True}]),
+                                'fromcurrent': True}]),
                 dict(label='Pause',
                      method='animate',
                      args=[[None], {'frame': {'duration': 0, 'redraw': False},
-                                    'mode': 'immediate',
-                                    'transition': {'duration': 0}}])
+                                  'mode': 'immediate',
+                                  'transition': {'duration': 0}}])
             ]
         }]
     )
     return fig_map
 
+
 def create_race_plot(df, year_range):
     # Prepare data for race plot
+    # Convert year columns to rows for raceplotly format
     df_race = df.melt(
         id_vars=['Country'],
-        value_vars=[str(year) for year in range(year_range[0], year_range[1] + 1)],
+        value_vars=range(year_range[0], year_range[1] + 1),
         var_name='Year',
         value_name='Emissions'
     )
     
-    # Create the race plot using barplot from raceplotly.plots
+    # Create the race plot
     race_plot = barplot(
         df_race,
         item_column='Country',
@@ -126,6 +122,7 @@ def create_race_plot(df, year_range):
         top_entries=10,
     )
     
+    # Plot with custom settings
     fig = race_plot.plot(
         title='Top 10 Countries by CO2 Emissions',
         orientation='horizontal',
@@ -134,28 +131,156 @@ def create_race_plot(df, year_range):
         time_label='Year: ',
         frame_duration=800
     )
+
+    # fig.update_layout(
+    # height=700,  # Make plot taller
+    # font=dict(size=12),  # Increase base font size
+    # title_font_size=20,  # Larger title
+    # xaxis_title_font_size=16,  # Larger axis titles
+    # yaxis_title_font_size=16,
+    # yaxis_tickfont_size=14,  # Larger tick labels
+    # xaxis_tickfont_size=14
+    # )
     
     return fig
 
+def create_covid_impact_plot(df1, df2):
+    # Create tabs for different COVID analyses
+    tab1, tab2 = st.tabs(["Global Impact", "Sectoral Impact"])
+    print(df2.head())
+    
+    with tab1:
+        # Global emissions around COVID
+        years_covid = range(2017, 2023)
+        global_emissions = df1[df1['Country'] == 'GLOBAL TOTAL']
+        emissions_covid = global_emissions[list(years_covid)].values[0]
+        
+        fig_global = px.line(
+            x=years_covid, 
+            y=emissions_covid,
+            title='Global CO2 Emissions Around COVID-19',
+            labels={'x': 'Year', 'y': 'CO2 Emissions (Mt CO2)'}
+        )
+        fig_global.add_vline(x=2020, line_dash="dash", line_color="red",
+                            annotation_text="COVID-19")
+        st.plotly_chart(fig_global, use_container_width=True)
+        
+        # Calculate and display percentage changes
+        col1, col2 = st.columns(2)
+        with col1:
+            change_2020 = ((emissions_covid[3] - emissions_covid[2])/emissions_covid[2]*100)
+            st.metric("2020 Emissions Change", f"{change_2020:.1f}%")
+        with col2:
+            change_2021 = ((emissions_covid[4] - emissions_covid[3])/emissions_covid[3]*100)
+            st.metric("2021 Recovery", f"{change_2021:.1f}%")
+    
+    with tab2:
+        # Sectoral analysis
+        sectors = ['Power Industry', 'Industrial Combustion', 'Transport', 'Processes']
+        sector_data = {}
+        
+        for sector in sectors:
+            sector_emissions = df2[(df2['Sector'] == sector) & 
+                                 (df2['Country'] == 'GLOBAL TOTAL')]
+            sector_data[sector] = sector_emissions[list(years_covid)].values[0]
+        
+        # Create DataFrame for plotly
+        df_sectors = pd.DataFrame(sector_data, index=years_covid).reset_index()
+        df_sectors_melted = df_sectors.melt('index', var_name='Sector', 
+                                          value_name='Emissions')
+        
+        fig_sectors = px.line(
+            df_sectors_melted,
+            x='index',
+            y='Emissions',
+            color='Sector',
+            title='CO2 Emissions by Sector Around COVID-19'
+        )
+        fig_sectors.add_vline(x=2020, line_dash="dash", line_color="red",
+                             annotation_text="COVID-19")
+        st.plotly_chart(fig_sectors, use_container_width=True)
+        
+        # Display sector-specific impacts
+        st.subheader("Sector Impact (2019-2020)")
+        cols = st.columns(len(sectors))
+        for i, sector in enumerate(sectors):
+            change = ((sector_data[sector][3] - sector_data[sector][2])/
+                     sector_data[sector][2]*100)
+            cols[i].metric(sector, f"{change:.1f}%")
+
+def create_agreements_timeline(df1):
+    # Get the global total data
+    global_data = df1[df1['Country'] == 'GLOBAL TOTAL'].iloc[0]
+    
+    # Get only the year columns (1970 to 2023)
+    year_columns = [col for col in df1.columns if str(col).isdigit()]
+    years = [int(col) for col in year_columns]
+    values = [global_data[year] for year in years]
+    
+    # Create the plot using plotly
+    fig = px.line(
+        x=years, 
+        y=values,
+        title='Global CO2 Emissions and Key Climate Agreements',
+        labels={'x': 'Year', 'y': 'CO2 Emissions (Mt CO2)'}
+    )
+    
+    # Add vertical lines for key agreements
+    agreements = {
+        1997: 'Kyoto Protocol Adopted',
+        2005: 'Kyoto Protocol Enforced',
+        2015: 'Paris Agreement'
+    }
+    
+    colors = {'1997': 'red', '2005': 'green', '2015': 'orange'}
+    
+    for year, agreement in agreements.items():
+        fig.add_vline(
+            x=year,
+            line_dash="dash",
+            line_color=colors[str(year)],
+            annotation_text=agreement,
+            annotation_position="top"
+        )
+    
+    # Customize layout
+    fig.update_layout(
+        hovermode='x unified',
+        showlegend=False,
+        height=600
+    )
+    
+    return fig
+
+
+
 # Main App Function
 def main():
     st.set_page_config(page_title="CO2 Emissions Dashboard", layout="wide")
     st.title("Global CO2 Emissions Dashboard")
     
-    # Load Data from SQLite Database
+    # Load Data
     df1 = load_data("fossil_CO2_per_capita_by_countr")
-    df = load_data("fossil_CO2_totals_by_country")
-    df = df[df['Country'] != 'International Shipping']
-    df2 = df.copy()[:210]
+    df2 = load_data("fossil_CO2_totals_by_country")
+    df3 = load_data("fossil_CO2_by_sector_country_su", year_range=(2017, 2023), sector="Sector")
+    df_covid = load_data("fossil_CO2_totals_by_country", year_range=(2017, 2023))
+    df_poliicy = load_data("fossil_CO2_totals_by_country", year_range=(1970,2023))
+    
+    df2 = df2[df2['Country'] != 'International Shipping']
+    df_only_countries = df2.copy()[:210]
     
     # Sidebar Controls
+    #st.sidebar.header("Controls")
+
+    #st.sidebar.markdown("---")
     st.sidebar.image("dati/SIAM-logo.jpg", width=150)
+
     visualization_type = st.sidebar.radio(
         "Choose Visualization",
-        ["Time Series Plot", "Animated World Map", "Bar Chart Race"]
+        ["Time Series Plot", "Animated World Map", "Bar Chart Race", "COVID-19 Impact", "Climate Agreements Timeline"]
     )
     
-    # Year range selector (common to all visualizations)
+    # Year range selector (common to both visualizations)
     year_range = st.sidebar.slider(
         "Select Year Range",
         min_value=YEAR_RANGE[0],
@@ -163,8 +288,9 @@ def main():
         value=YEAR_RANGE
     )
     
+    # Conditional controls and display
     if visualization_type == "Time Series Plot":
-        st.subheader("CO2 per capita Emissions Time Series")
+        st.subheader("CO2 Emissions Time Series")
         
         # Show country selector only for time series
         countries = df1['Country'].unique().tolist()
@@ -183,13 +309,54 @@ def main():
         st.subheader("Global Emissions Map (Animated)")
         fig_map = create_animated_choropleth(df1, year_range[0], year_range[1])
         st.plotly_chart(fig_map, use_container_width=True)
+
+    elif visualization_type == "Climate Agreements Timeline":
+        st.subheader("Global Emissions and Climate Agreements")
+        
+        # Add some context about the agreements
+        with st.expander("About the Climate Agreements"):
+            st.markdown("""
+            - **Kyoto Protocol (1997)**: First legally binding agreement to reduce greenhouse gases
+            - **Kyoto Protocol Enforcement (2005)**: The protocol came into force
+            - **Paris Agreement (2015)**: Global agreement to limit temperature rise to well below 2°C
+            """)
+        
+        # Create and display the plot
+        fig = create_agreements_timeline(df_poliicy)
+        st.plotly_chart(fig, use_container_width=True)
+        
+        # Add some analysis
+        st.markdown("### Key Observations")
+        col1, col2, col3 = st.columns(3)
+        
+        # Calculate some metrics
+        kyoto_change = ((float(df_poliicy[df_poliicy['Country'] == 'GLOBAL TOTAL'][2005]) - 
+                        float(df_poliicy[df_poliicy['Country'] == 'GLOBAL TOTAL'][1997])) / 
+                       float(df_poliicy[df_poliicy['Country'] == 'GLOBAL TOTAL'][1997]) * 100)
+        
+        paris_change = ((float(df_poliicy[df_poliicy['Country'] == 'GLOBAL TOTAL'][2023]) - 
+                        float(df_poliicy[df_poliicy['Country'] == 'GLOBAL TOTAL'][2015])) / 
+                       float(df_poliicy[df_poliicy['Country'] == 'GLOBAL TOTAL'][2015]) * 100)
+        
+        with col1:
+            st.metric("Emissions Change 1997-2005", f"{kyoto_change:.1f}%")
+        with col2:
+            st.metric("Emissions Change 2015-2023", f"{paris_change:.1f}%")
+        with col3:
+            latest_emissions = float(df_poliicy[df_poliicy['Country'] == 'GLOBAL TOTAL'][2023])
+            st.metric("Current Emissions (Mt CO2)", f"{latest_emissions:.1f}")
+
+    elif visualization_type == "COVID-19 Impact":
+        st.subheader("COVID-19 Impact Analysis")
+        create_covid_impact_plot(df_covid, df3)
+
     else:
         st.subheader("Top 10 CO2 Emitters Race")
-        fig_race = create_race_plot(df2, year_range)
+        fig_race = create_race_plot(df_only_countries, year_range)
         st.plotly_chart(fig_race, use_container_width=True)
     
     st.sidebar.markdown("---")
-    st.sidebar.markdown("""GRUPPO 5 (EMANUELA, FULVIO, MARCO, TINSAE)""")
+    st.sidebar.markdown(""" GRUPPO 5 (EMANUELA, FULVIO, MARCO, TINSAE) """)
 
 if __name__ == "__main__":
-    main()
+    main()

data.db

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:d414397c6b70f8f56ffd35754b42cbf4fc3e4b6b08b3ed533ef109d66dc03530
-size 245760
+oid sha256:8352a360b4a80aa7a63b498b8c9e1e57b3e3663f6bf86b59b3f9cdd25a30103e
+size 1150976